Impervious land classification using bootstrap principal component analysis

Tarumanagara International Conference on the Applications of Technology and Engineering

工业技术（总论）

Hendryli, Janson^1 ; Herwindiati, Dyah Erny^1 ; Merdi, Junita^1

Informatics Department, Faculty of Information Technology Universitas Tarumanagara, Jakarta

11440, Indonesia^1

关键词: Cohen's kappas;    Impervious surface;    Land areas;    Land cover;    Megacities;    Remote sensing technology;    Urban development;    Water areas;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/508/1/012116/pdf
DOI  :  10.1088/1757-899X/508/1/012116

学科分类：工业工程学

来源: IOP

【 摘 要 】

This paper proposes the bootstrap principal component analysis for the classification of an impervious land area. The impervious surface is closely related to the urban development area, and therefore, crucial to the urban planning of the city. In this paper, we use the remote sensing technology to map each pixel of the images as impervious land, water area, or empty lands. The case study is the Jabodetabek area in Indonesia, which is a megapolitan area consisting of megacities of Jakarta, Bogor, Depok, Tangerang, and Bekasi. The area is the political and economic center of the country. We evaluate the model using Cohen's kappa coefficient, which shows that the model has excellent performance in classifying those three land-cover classes.